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CHHandling 1 Million RPS isn’t about code — it’s about smart architecture.
1️⃣ Traffic Distribution (Load Balancers)
➡️ Spreads incoming requests across many servers so nothing overloads.
Example: 1M requests split across 200 servers = ~5K requests per server.
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2️⃣ Scale Out, Not Up (Horizontal Scaling)
➡️ Add more machines instead of making one server bigger.
Example: Flash sale traffic? Instantly launch 50 new API instances.
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3️⃣ Fast Reads with Cache
➡️ Use Redis/Memcached to avoid hitting the database every time.
Example: Cached user data = millions of DB calls saved daily.
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4️⃣ Edge Delivery with CDN
➡️ Static content loads from servers closest to the user.
Example: Users in Delhi fetch images from a Delhi CDN node.
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5️⃣ Background Work with Queues
➡️ Heavy tasks run asynchronously so APIs respond instantly.
Example: Payment succeeds now, email receipt sent in background.
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6️⃣ Split the Database (Sharding)
➡️ Divide data across multiple databases to handle scale.
Example: Usernames A–M on one shard, N–Z on another.
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7️⃣ Rate Limiting
➡️ Prevent abuse and traffic spikes from taking the system down.
Example: Limit clients to 100 requests/sec to block bots from killing the API.
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8️⃣ Lightweights Payloads
➡️ Smaller payloads = faster responses + less bandwidth.
Example: Send only required fields instead of massive JSON blobs.
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[API Design] [System Architecture] [API Scaling] [1 Million RPS] [Distributed Systems] [Load Balancing] [Database Sharding] [High Availability]
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